Jiaying Toh

Viral pandemics and epidemics pose a significant global threat, with emerging and re-emerging viruses responsible for four pandemics in the 21st century alone. While macaques have been utilized as a model for understanding viral disease in a controlled setting, it remains unclear how conserved the antiviral responses to diverse viruses are between macaques and humans. To address this critical knowledge gap, we conducted a comprehensive cross-species analysis of transcriptomic data from over 6000 blood samples from macaques and humans infected with one of 31 viruses, including Lassa, Ebola, Marburg, Zika, and dengue. Our findings demonstrate that irrespective of primate or viral species, there are conserved antiviral responses which are consistent regardless of infection phase (acute, chronic, or latent) and viral genome type (DNA or RNA viruses). Moreover, by leveraging longitudinal data from experimental challenges, we identified virus-specific response dynamics such as host responses to Coronaviridae and Orthomyxoviridae infections peaking 1-3 days earlier than responses to Filoviridae and Arenaviridae viral infections. Additionally, through comparative analysis of immune responses across viruses, we identified a unique enrichment of lymphoid cellular response modules in macaque Flaviviridae infection that persists in human responses to dengue. Our results underscore macaque studies as a powerful tool for gaining new insights into viral pathogenesis and immune responses that translate to humans, which can inform viral therapeutic development and enable pandemic preparedness. One sentence summary: Using longitudinal macaque viral challenge studies, we identified shared and virus-specific responses to infection that replicate in human viral disease - thereby demonstrating the utility of macaque models of viral infection to understand antiviral biology and for pandemic preparedness.

Background: Determining the severity of COVID-19 remains an unmet medical need. Our objective was to develop a blood-based host-gene-expression classifier for the severity of viral infections and validate it in independent data, including COVID-19. Methods: We developed the classifier for the severity of viral infections and validated it in multiple viral infection settings including COVID-19. We used training data (N=705) from 21 retrospective transcriptomic clinical studies of influenza and other viral illnesses looking at a preselected panel of host immune response messenger RNAs. Results: We selected 6 host RNAs and trained logistic regression classifier with a cross-validation area under curve of 0.90 for predicting 30-day mortality in viral illnesses. Next, in 1,417 samples across 21 independent retrospective cohorts the locked 6-RNA classifier had an area under curve of 0.91 for discriminating patients with severe vs. non-severe infection. Next, in independent cohorts of prospectively (N=97) and retrospectively (N=100) enrolled patients with confirmed COVID-19, the classifier had an area under curve of 0.89 and 0.87, respectively, for identifying patients with severe respiratory failure or 30-day mortality. Finally, we developed a loop-mediated isothermal gene expression assay for the 6-messenger-RNA panel to facilitate implementation as a rapid assay. Conclusions: With further study, the classifier could assist in the risk assessment of COVID-19 and other acute viral infections patients to determine severity and level of care, thereby improving patient management and reducing healthcare burden.

SARS-CoV-2 pandemic, the fourth pandemic of the decade, has underscored gaps in global pandemic preparedness and the need for generalizable tests to avert overwhelming healthcare systems worldwide, irrespective of a virus. We integrated 4,780 blood transcriptome profiles from patients infected with one of 16 viruses across 34 independent cohorts from 18 countries, and 71 scRNA-seq profiles of 264,224 immune cells across three independent cohorts. We found a myeloid cell-dominated conserved host response associated with severity. It showed increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells with increased severity. We identified four gene modules that delineate distinct trajectories associated with mild and severe outcomes, and show the interferon response was decoupled from protective host response during severe viral infection. These modules distinguished non-severe from severe viral infection with clinically useful accuracy. Together, our findings provide insights into immune response dynamics during viral infection, and identify factors that may influence patient outcomes.

COVID-19 is a pandemic that shares certain clinical characteristics with other acute viral infections. Here, we studied the whole-blood transcriptomic host response to SARS-CoV-2 and compared it with other viral infections to understand similarities and differences in host response. Using RNAseq we profiled peripheral blood from 24 healthy controls and 62 prospectively enrolled patients with community-acquired lower respiratory tract infection by SARS-Cov-2 within the first 24 hours of hospital admission. We also compiled and curated 23 independent studies that profiled 1,855 blood samples from patients with one of six viruses (influenza, RSV, HRV, ebola, Dengue, and SARS-CoV-1). We show gene expression changes in peripheral blood in patients with COVID-19 versus healthy controls are highly correlated with changes in response to other viral infections (r=0.74, pACO1 and ATL3 , show significantly opposite changes between conditions. Pathway analysis in patients with COVID-19 or other viral infections versus healthy controls identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection, and cytokine production for over-expressed genes. Conversely, for under-expressed genes, pathways indicated repression of lymphocyte differentiation and T cell activation. When comparing transcriptome profiles of patients with COVID-19 directly with those with other viral infections, we found 114 and 302 genes were over- or under-expressed, respectively, during COVID-19. Pathways analysis did not identify any significant pathways in these genes, suggesting novel responses to further study. Statistical deconvolution using immunoStates found that M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells, and total B cells showed change consistently in the same direction across all viral infections including COVID-19. Those that increased in COVID-19 but decreased in non-COVID-19 viral infections were CD56 ^bright NK cells, M2 macrophages, and total NK cells. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of COVID-19 versus other viral infections and show clear differences in signaling pathways and cellularity as part of the host response to SARS-CoV-2.

Epstein-Barr Virus (EBV) is a ubiquitous virus linked to a variety of lymphoid and epithelial malignancies. In solid organ and hematopoietic stem cell transplant recipients, EBV is causally associated with posttransplant lymphoproliferative disorder (PTLD), a group of heterogeneous lymphoid diseases. EBV ⁺ B cell lymphomas that develop in the context of PTLD are generally attributed to the immunosuppression required to promote graft survival, but little is known regarding the role of EBV genome diversity in the development of malignancy. We deep-sequenced the EBV genome from the peripheral blood of 18 solid organ transplant recipients, including 6 PTLD patients. Sequences from 6 EBV ⁺ spontaneous lymphoblastoid B cell lines (SLCL) were similarly analyzed. The EBV genome from PTLD patients had a significantly greater number of variations than EBV from transplant recipients without PTLD. Importantly, there were 15 nonsynonymous variations, including 8 in the latent cycle gene EBNA3C that were associated with the development of PTLD. One of the nonsynonymous variations in EBNA3C is located within a previously defined T cell epitope. These findings suggest that variations in the EBV genome can contribute to the pathogenesis of PTLD.